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DtreeMethods.py
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DtreeMethods.py
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<<<<<<< HEAD
from DtreeNodes import leafNode
from DtreeNodes import questionNode
import math
from enum import Enum
class DtreeMethods:
@staticmethod
def find_best_attribute(dataset):
num_columns = len(dataset[0])
list_of_pi = []
set_of_classes = set()
# get all the possible classes
for example in dataset:
set_of_classes.add(example[num_columns - 1])
for c in set_of_classes:
list_of_pi.append(DtreeMethods.__get_pi(dataset, num_columns - 1, c, c))
h_t = DtreeMethods.__calc_entropy(list_of_pi)
# H(T, attribute) values
h_t_attributes = {}
collection_of_attribute_value_entropies = {}
# calculate the H(T, attribute)
for column in range(1, len(dataset[0]) - 1):
# H(attribute = value) values
h_attributes_values = {}
# get possible values from column
possible_values = set()
for example in dataset:
possible_values.add(example[column])
# calculate the H(attribute = value) for each possible value
for value in possible_values:
list_of_pi = []
for c in set_of_classes:
list_of_pi.append(DtreeMethods.__get_pi(dataset, column, value, c))
entropy = DtreeMethods.__calc_entropy(list_of_pi)
h_attributes_values[value] = entropy
collection_of_attribute_value_entropies[(column, value)] = entropy
# calculate average entropy
average_entropy = 0
for value in possible_values:
average_entropy += DtreeMethods.__get_relative_freq(dataset, column, value) * h_attributes_values[value]
h_t_attributes[column] = average_entropy
# find attribute with most info gain
highest_gain = 0
best_attribute = ""
attribute_info_gains = {}
for attribute in h_t_attributes:
current_gain = h_t - h_t_attributes[attribute]
attribute_info_gains[attribute] = current_gain
if current_gain > highest_gain:
highest_gain = current_gain
best_attribute = attribute
# case of no best attribute to split on. this means we will have a leaf
if best_attribute == "":
best_attribute = -1
# returns selected attribute, attribute value entropies, attribute entropies, and attribute info gains
return best_attribute, collection_of_attribute_value_entropies, h_t_attributes, attribute_info_gains
@staticmethod
def __get_relative_freq(dataset, attribute, value):
num_total_examples = 0
num_examples_with_value = 0
for example in dataset:
num_total_examples += 1
if example[attribute] == value:
num_examples_with_value += 1
if num_total_examples == 0:
return 0
return num_examples_with_value / num_total_examples
@staticmethod
def __calc_entropy(list_of_pi):
entropy = 0
for pi in list_of_pi:
if pi == 0:
entropy += 0
else:
entropy += -pi * math.log2(pi)
return entropy
@staticmethod
def __get_pi(dataset, attribute, value, label):
num_examples_with_value = 0
num_examples_with_value_with_label = 0
num_total_examples = 0
num_columns = len(dataset[0])
# find number of examples with the value of a given attribute
for example in dataset:
num_total_examples += 1
if example[attribute] == value:
num_examples_with_value += 1
if example[num_columns - 1] == label:
num_examples_with_value_with_label += 1
if num_examples_with_value == 0:
return 0
if attribute == num_columns - 1:
return num_examples_with_value / num_total_examples
return num_examples_with_value_with_label / num_examples_with_value
@staticmethod
def print_attribute_data(best_attribute_data, COLUMNS):
print("best attribute to split on:", COLUMNS(best_attribute_data[0]).name, "column:",
best_attribute_data[0], "\n")
print("attribute value entropies:")
for attribute_value_tuple in best_attribute_data[1]:
print(COLUMNS(attribute_value_tuple[0]).name, attribute_value_tuple[1],
best_attribute_data[1][attribute_value_tuple])
print()
print("attribute entropies:")
for attribute in best_attribute_data[2]:
print(COLUMNS(attribute).name, best_attribute_data[2][attribute])
print()
print("attribute information gains:")
for attribute in best_attribute_data[3]:
print(COLUMNS(attribute).name, best_attribute_data[3][attribute])
print()
@staticmethod
def getClassification(node, example):
if node is type leafNode:
return node.getClassification()
else node is type questionNode:
attribute = node.getAttribute()
value = example[attribute]
getClassification(node.getChild(value), example)
"""
Build the Decision Tree.
"""
# return best_attribute, collection_of_attribute_value_entropies, h_t_attributes, attribute_info_gains
def build_tree(dataset):
list_of_subsets = []
# find the best attribute for current data subset
best_attribute = DtreeMethods.find_best_attribute(dataset)
#create question node using the best attribute
q_node = questionNode(best_attribute[0])
# divide dataset into subsets i.e shape, fillling size
list_of_subsets = DtreeMethods.divide_set_by_attribute(best_attribute[0], dataset)
print("attriubte ",best_attribute[0] )
for subset in list_of_subsets:
if DtreeMethods.__is_same_class(subset[1]):
# add new leafNode
new_class = DtreeMethods.__get_class(subset[1][0])
child_node = leafNode(new_class)
# add the class with its subset
q_node.addChild(subset[1], child_node.classification)
print("adding child node: ", child_node.classification)
print("its children are: ", q_node.getChild(child_node.classification))
else:
DtreeMethods.build_tree(subset[1])
return q_node
# divide the data set by the given attribute
# i.e attribute is shape --- return 3 subsets !
# return list of tuple (square, list_of_square_subset)...
def divide_set_by_attribute(attribute, dataset):
list_of_subset = []
# list of unqiue values in the given attribute:
unique_values = DtreeMethods.__get_unique_values_for_attribute(attribute, dataset)
for value in unique_values:
subset=[]
# for each vector in dataset
for example in dataset:
# print("example: ", example)
if example[attribute] == value :
subset.append(example)
list_of_subset.append((value, subset))
return list_of_subset
# return true if all vectors in the subset have the same class
@staticmethod
def __is_same_class(subset):
num_columns = len(subset[0])
this_class = subset[0][num_columns - 1]
for i in range(1, len(subset)):
if subset[i][num_columns - 1] != this_class:
return False
return True
# return subsets of each attribute
# shape: return cirlce, square, triangle
@staticmethod
def __get_unique_values_for_attribute(attribute, dataset):
subset = []
for example in dataset:
# print(example)
# print('attribute: ', example[attribute])
if not subset:
subset.append(example[attribute])
if example[attribute] not in subset:
subset.append(example[attribute])
# print(subset)
return subset
# return the class of each vector
@staticmethod
def __get_class(data):
num_columns = len(data)
return data[num_columns - 1]
def main():
class PIE_COLUMNS(Enum):
NO_ATTRIBUTE = -1
ID = 0
CRUST_SIZE = 1
SHAPE = 2
FILLING_SIZE = 3
CLASS = 4
# data must be in order from crust size, shape, filling size and class, according to the Enum
pie_data = [
["1", "big", "circle", "small", "pos"],
["2", "small", "circle", "small", "pos"],
["3", "big", "square", "small", "neg"],
["4", "big", "triangle", "small", "neg"],
["5", "big", "square", "big", "pos"],
["6", "small", "square", "small", "neg"],
["7", "small", "square", "big", "pos"],
["8", "big", "circle", "big", "pos"],
]
best_pie_attribute_data = DtreeMethods.find_best_attribute(pie_data)
print("best attribute in pies domain to split on:", PIE_COLUMNS(best_pie_attribute_data[0]).name, "\n")
print("pie domain attribute value entropies:")
for attribute_value_tuple in best_pie_attribute_data[1]:
print(PIE_COLUMNS(attribute_value_tuple[0]).name, attribute_value_tuple[1],
best_pie_attribute_data[1][attribute_value_tuple])
print()
print("pie domain attribute entropies:")
for attribute in best_pie_attribute_data[2]:
print(PIE_COLUMNS(attribute).name, best_pie_attribute_data[2][attribute])
print()
print("pie domain attribute information gains:")
for attribute in best_pie_attribute_data[3]:
print(PIE_COLUMNS(attribute).name, best_pie_attribute_data[3][attribute])
print()
class CHESS_COLUMNS(Enum):
NO_ATTRIBUTE = -1
ID = 0
WHITE_KING_FILE = 1
WHITE_KING_RANK = 2
WHITE_ROOK_FILE = 3
WHITE_ROOK_RANK = 4
BLACK_KING_FILE = 5
BLACK_KING_RANK = 6
CLASS = 7
# attributes are in order by the Enum
chess_data = [
["1", "d", "1", "f", "3", "e", "4", "draw"],
["2", "a", "1", "f", "3", "g", "3", "draw"],
["3", "c", "2", "d", "6", "a", "1", "one"],
["4", "d", "2", "e", "8", "a", "1", "four"],
["5", "c", "3", "e", "8", "c", "1", "two"],
["6", "c", "3", "d", "4", "e", "1", "eight"],
["7", "d", "3", "a", "8", "f", "3", "nine"],
["8", "d", "3", "e", "2", "b", "1", "four"],
["9", "d", "3", "b", "8", "b", "1", "three"],
]
best_chess_attribute_data = DtreeMethods.find_best_attribute(chess_data)
print("best attribute in chess domain to split on:", CHESS_COLUMNS(best_chess_attribute_data[0]).name, "\n")
print("chess domain attribute value entropies:")
for attribute_value_tuple in best_chess_attribute_data[1]:
print(CHESS_COLUMNS(attribute_value_tuple[0]).name, attribute_value_tuple[1],
best_chess_attribute_data[1][attribute_value_tuple])
print()
print("chess domain attribute entropies:")
for attribute in best_chess_attribute_data[2]:
print(CHESS_COLUMNS(attribute).name, best_chess_attribute_data[2][attribute])
print()
print("chess domain attribute information gains:")
for attribute in best_chess_attribute_data[3]:
print(CHESS_COLUMNS(attribute).name, best_chess_attribute_data[3][attribute])
print()
# test divide_set_by_attribute
list_of_subset = DtreeMethods.divide_set_by_attribute(1, chess_data)
for subset in list_of_subset:
print("subset by : " ,subset[0], subset[1])
# to test need to change this method to public
# print("class for this subset: ", DtreeMethods.get_class(subset[1][0]))
# test build tree
question_node = DtreeMethods.build_tree(pie_data)
if __name__ == "__main__":
main()
=======
# from DtreeNodes import leafNode
# from DtreeNodes import questionNode
# import math
# import random
# from enum import Enum
#
#
#
# class DtreeMethods:
#
# @staticmethod
# def find_best_attribute(dataset):
# num_columns = len(dataset[0])
# list_of_pi = []
# set_of_classes = set()
# # get all the possible classes
# for example in dataset:
# set_of_classes.add(example[num_columns - 1])
# for c in set_of_classes:
# list_of_pi.append(DtreeMethods.__get_pi(dataset, num_columns - 1, c, c))
#
# h_t = DtreeMethods.__calc_entropy(list_of_pi)
#
# # H(T, attribute) values
# h_t_attributes = {}
# collection_of_attribute_value_entropies = {}
#
# # calculate the H(T, attribute)
# for column in range(1, len(dataset[0]) - 1):
# # H(attribute = value) values
# h_attributes_values = {}
#
# # get possible values from column
# possible_values = set()
# for example in dataset:
# possible_values.add(example[column])
# # calculate the H(attribute = value) for each possible value
# for value in possible_values:
# list_of_pi = []
# for c in set_of_classes:
# list_of_pi.append(DtreeMethods.__get_pi(dataset, column, value, c))
# entropy = DtreeMethods.__calc_entropy(list_of_pi)
#
# h_attributes_values[value] = entropy
# collection_of_attribute_value_entropies[(column, value)] = entropy
#
# # calculate average entropy
# average_entropy = 0
# for value in possible_values:
# average_entropy += DtreeMethods.__get_relative_freq(dataset, column, value) * h_attributes_values[value]
# h_t_attributes[column] = average_entropy
#
# # find attribute with most info gain
# highest_gain = 0
# best_attribute = ""
# attribute_info_gains = {}
# for attribute in h_t_attributes:
# current_gain = h_t - h_t_attributes[attribute]
# attribute_info_gains[attribute] = current_gain
# if current_gain > highest_gain:
# highest_gain = current_gain
# best_attribute = attribute
#
# # case of no best attribute to split on. this means we will have a leaf
# if best_attribute == "":
# best_attribute = -1
# # returns selected attribute, attribute value entropies, attribute entropies, and attribute info gains
# return best_attribute, collection_of_attribute_value_entropies, h_t_attributes, attribute_info_gains
#
# @staticmethod
# def __get_relative_freq(dataset, attribute, value):
# num_total_examples = 0
# num_examples_with_value = 0
# for example in dataset:
# num_total_examples += 1
# if example[attribute] == value:
# num_examples_with_value += 1
#
# if num_total_examples == 0:
# return 0
# return num_examples_with_value / num_total_examples
#
# @staticmethod
# def __calc_entropy(list_of_pi):
# entropy = 0
# for pi in list_of_pi:
# if pi == 0:
# entropy += 0
# else:
# entropy += -pi * math.log2(pi)
# return entropy
#
# @staticmethod
# def __get_pi(dataset, attribute, value, label):
# num_examples_with_value = 0
# num_examples_with_value_with_label = 0
# num_total_examples = 0
# num_columns = len(dataset[0])
#
# # find number of examples with the value of a given attribute
# for example in dataset:
# num_total_examples += 1
# if example[attribute] == value:
# num_examples_with_value += 1
#
# if example[num_columns - 1] == label:
# num_examples_with_value_with_label += 1
#
# if num_examples_with_value == 0:
# return 0
#
# if attribute == num_columns - 1:
# return num_examples_with_value / num_total_examples
#
# return num_examples_with_value_with_label / num_examples_with_value
#
# @staticmethod
# def print_attribute_data(best_attribute_data, COLUMNS):
# print("best attribute to split on:", COLUMNS(best_attribute_data[0]).name, "column:",
# best_attribute_data[0], "\n")
# print("attribute value entropies:")
# for attribute_value_tuple in best_attribute_data[1]:
# print(COLUMNS(attribute_value_tuple[0]).name, attribute_value_tuple[1],
# best_attribute_data[1][attribute_value_tuple])
# print()
# print("attribute entropies:")
# for attribute in best_attribute_data[2]:
# print(COLUMNS(attribute).name, best_attribute_data[2][attribute])
# print()
# print("attribute information gains:")
# for attribute in best_attribute_data[3]:
# print(COLUMNS(attribute).name, best_attribute_data[3][attribute])
# print()
#
#
# @staticmethod
# def getClassification(node, example, possible_labels):
# if isinstance(node, leafNode):
# return node.getLabel()
# elif isinstance(node, questionNode):
# attribute = node.getAttribute()
# value = example[attribute]
# # need to check for missing edge
# if value in node.children:
# return DtreeMethods.getClassification(node.getChild(value), example, possible_labels)
# else:
# # we have a missing edge, need to give a random class
# random_index = random.randint(0, len(possible_labels) - 1)
# return possible_labels[random_index]
#
# @staticmethod
# def get_possible_labels_from_data(dataset):
# set_of_possible_classes = set()
# num_columns = len(dataset[0])
# for example in dataset:
# set_of_possible_classes.add(example[num_columns - 1])
# return list(set_of_possible_classes)
#
#
# """
# Build the Decision Tree.
# """
# # return best_attribute, collection_of_attribute_value_entropies, h_t_attributes, attribute_info_gains
# @staticmethod
# def build_tree(dataset):
# list_of_subsets = []
# # find the best attribute for current data subset
# best_attribute = DtreeMethods.find_best_attribute(dataset)
#
# #create question node using the best attribute
# q_node = questionNode(best_attribute[0])
# # divide dataset into subsets i.e shape, fillling size
# list_of_subsets = DtreeMethods.divide_set_by_attribute(best_attribute[0], dataset)
#
# for subset in list_of_subsets:
# if DtreeMethods.__is_same_class(subset[1]):
# # add new leafNode
# new_class = DtreeMethods.__get_class(subset[1][0])
# child_node = leafNode(new_class)
# # add the class with its subset
# # print("subset[1] ", subset[1])
# q_node.addChild(subset[0], child_node)
# #print("adding child node: ", child_node.label)
# #print("its children are: ", q_node.getChild(child_node.label))
# else:
# q_node.addChild(subset[0], DtreeMethods.build_tree(subset[1]))
# return q_node
#
#
# # divide the data set by the given attribute
# # i.e attribute is shape --- return 3 subsets !
# # return list of tuple (square, list_of_square_subset)...
# @staticmethod
# def divide_set_by_attribute(attribute, dataset):
# list_of_subset = []
# # list of unqiue values in the given attribute:
# unique_values = DtreeMethods.__get_unique_values_for_attribute(attribute, dataset)
# for value in unique_values:
# subset=[]
# # for each vector in dataset
# for example in dataset:
# # print("example: ", example)
# if example[attribute] == value :
# subset.append(example)
#
# list_of_subset.append((value, subset))
#
#
# return list_of_subset
#
#
#
# # return true if all vectors in the subset have the same class
# @staticmethod
# def __is_same_class(subset):
# num_columns = len(subset[0])
# this_class = subset[0][num_columns - 1]
# for i in range(1, len(subset)):
# if subset[i][num_columns - 1] != this_class:
# return False
# return True
#
#
# # return subsets of each attribute
# # shape: return cirlce, square, triangle
# @staticmethod
# def __get_unique_values_for_attribute(attribute, dataset):
# subset = []
# for example in dataset:
# # print(example)
# # print('attribute: ', example[attribute])
# if not subset:
# subset.append(example[attribute])
# if example[attribute] not in subset:
# subset.append(example[attribute])
# # print(subset)
# return subset
#
# # return the class of each vector
# @staticmethod
# def __get_class(data):
# num_columns = len(data)
# return data[num_columns - 1]
#
#
# def main():
# class PIE_COLUMNS(Enum):
# NO_ATTRIBUTE = -1
# ID = 0
# CRUST_SIZE = 1
# SHAPE = 2
# FILLING_SIZE = 3
# CLASS = 4
#
# # data must be in order from crust size, shape, filling size and class, according to the Enum
# pie_data = [
# ["1", "big", "circle", "small", "pos"],
# ["2", "small", "circle", "small", "pos"],
# ["3", "big", "square", "small", "neg"],
# ["4", "big", "triangle", "small", "neg"],
# ["5", "big", "square", "big", "pos"],
# ["6", "small", "square", "small", "neg"],
# ["7", "small", "square", "big", "pos"],
# ["8", "big", "circle", "big", "pos"],
# ]
#
# best_pie_attribute_data = DtreeMethods.find_best_attribute(pie_data)
# print("best attribute in pies domain to split on:", PIE_COLUMNS(best_pie_attribute_data[0]).name, "\n")
# print("pie domain attribute value entropies:")
# for attribute_value_tuple in best_pie_attribute_data[1]:
# print(PIE_COLUMNS(attribute_value_tuple[0]).name, attribute_value_tuple[1],
# best_pie_attribute_data[1][attribute_value_tuple])
# print()
# print("pie domain attribute entropies:")
# for attribute in best_pie_attribute_data[2]:
# print(PIE_COLUMNS(attribute).name, best_pie_attribute_data[2][attribute])
# print()
# print("pie domain attribute information gains:")
# for attribute in best_pie_attribute_data[3]:
# print(PIE_COLUMNS(attribute).name, best_pie_attribute_data[3][attribute])
# print()
#
# class CHESS_COLUMNS(Enum):
# NO_ATTRIBUTE = -1
# ID = 0
# WHITE_KING_FILE = 1
# WHITE_KING_RANK = 2
# WHITE_ROOK_FILE = 3
# WHITE_ROOK_RANK = 4
# BLACK_KING_FILE = 5
# BLACK_KING_RANK = 6
# CLASS = 7
#
# # attributes are in order by the Enum
# chess_data = [
# ["1", "d", "1", "f", "3", "e", "4", "draw"],
# ["2", "a", "1", "f", "3", "g", "3", "draw"],
# ["3", "c", "2", "d", "6", "a", "1", "one"],
# ["4", "d", "2", "e", "8", "a", "1", "four"],
# ["5", "c", "3", "e", "8", "c", "1", "two"],
# ["6", "c", "3", "d", "4", "e", "1", "eight"],
# ["7", "d", "3", "a", "8", "f", "3", "nine"],
# ["8", "d", "3", "e", "2", "b", "1", "four"],
# ["9", "d", "3", "b", "8", "b", "1", "three"],
# ]
#
# best_chess_attribute_data = DtreeMethods.find_best_attribute(chess_data)
# print("best attribute in chess domain to split on:", CHESS_COLUMNS(best_chess_attribute_data[0]).name, "\n")
# print("chess domain attribute value entropies:")
# for attribute_value_tuple in best_chess_attribute_data[1]:
# print(CHESS_COLUMNS(attribute_value_tuple[0]).name, attribute_value_tuple[1],
# best_chess_attribute_data[1][attribute_value_tuple])
# print()
# print("chess domain attribute entropies:")
# for attribute in best_chess_attribute_data[2]:
# print(CHESS_COLUMNS(attribute).name, best_chess_attribute_data[2][attribute])
# print()
# print("chess domain attribute information gains:")
# for attribute in best_chess_attribute_data[3]:
# print(CHESS_COLUMNS(attribute).name, best_chess_attribute_data[3][attribute])
# print()
#
# # test divide_set_by_attribute
# list_of_subset = DtreeMethods.divide_set_by_attribute(1, chess_data)
# for subset in list_of_subset:
# print("subset by : " ,subset[0], subset[1])
# # to test need to change this method to public
# # print("class for this subset: ", DtreeMethods.get_class(subset[1][0]))
# # test build tree
# question_node = DtreeMethods.build_tree(chess_data)
#
#
# if __name__ == "__main__":
# main()
>>>>>>> d6f978a74bcfc1ea95dbd79ea406d1a3f7e3082b